# ifndef CPPAD_EXAMPLE_DEPRECATED_OLD_MAT_MUL_HPP
# define CPPAD_EXAMPLE_DEPRECATED_OLD_MAT_MUL_HPP

/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin old_mat_mul.hpp$$
$spell
	old_mat_mul.hpp
	cppad
	CppAD
	namespace
	struct
	nr
	nc
	bool
	vx
	const
	im
	mj
	ij
	px
	py
	std
	tx
	ty
	resize
	nz
	var
	jac
	Jacobian
	hes
	vy
$$


$section Define Matrix Multiply as a User Atomic Operation$$
$mindex old_mat_mul old_atomic test$$


$head Syntax$$
This file is located in the $code example$$ directory.
It can be copied to the current working directory and included
with the syntax
$codei%
	# include "old_mat_mul.hpp"
%$$

$head Example$$
The file $cref old_mat_mul.cpp$$ contains an example use of
$code old_mat_mul.hpp$$.
It returns true if it succeeds and false otherwise.

$head Begin Source$$
$srccode%cpp% */
# include <cppad/cppad.hpp>      // Include CppAD definitions
namespace {                      // Begin empty namespace
	using CppAD::vector;        // Let vector denote CppAD::vector
/* %$$

$head Extra Call Information$$
$srccode%cpp% */
	// Information we will attach to each mat_mul call
	struct call_info {
		size_t nr_result;
		size_t n_middle;
		size_t nc_result;
		vector<bool>  vx;
	};
	vector<call_info> info_; // vector of call information

	// number of orders for this operation (k + 1)
	size_t n_order_ = 0;
	// number of rows in the result matrix
	size_t nr_result_ = 0;
	// number of columns in left matrix and number of rows in right matrix
	size_t n_middle_ = 0;
	// number of columns in the result matrix
	size_t nc_result_ = 0;
	// which components of x are variables
	vector<bool>* vx_ = CPPAD_NULL;

	// get the information corresponding to this call
	void get_info(size_t id, size_t k, size_t n, size_t m)
	{	n_order_   = k + 1;
		nr_result_ = info_[id].nr_result;
		n_middle_  = info_[id].n_middle;
		nc_result_ = info_[id].nc_result;
		vx_        = &(info_[id].vx);

		assert(n == nr_result_ * n_middle_ + n_middle_ * nc_result_);
		assert(m ==  nr_result_ * nc_result_);
	}

/* %$$
$head Matrix Indexing$$
$srccode%cpp% */
	// Convert left matrix index pair and order to a single argument index
	size_t left(size_t i, size_t j, size_t ell)
	{	assert( i < nr_result_ );
		assert( j < n_middle_ );
		return (i * n_middle_ + j) * n_order_ + ell;
	}
	// Convert right matrix index pair and order to a single argument index
	size_t right(size_t i, size_t j, size_t ell)
	{	assert( i < n_middle_ );
		assert( j < nc_result_ );
		size_t offset = nr_result_ * n_middle_;
		return (offset + i * nc_result_ + j) * n_order_ + ell;
	}
	// Convert result matrix index pair and order to a single result index
	size_t result(size_t i, size_t j, size_t ell)
	{	assert( i < nr_result_ );
		assert( j < nc_result_ );
		return (i * nc_result_ + j) * n_order_ + ell;
	}
/* %$$

$head One Matrix Multiply$$
Forward mode matrix multiply left times right and sum into result:
$srccode%cpp% */
	void multiply_and_sum(
		size_t                order_left ,
		size_t                order_right,
		const vector<double>&         tx ,
		vector<double>&               ty )
	{	size_t i, j;
		size_t order_result = order_left + order_right;
		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
			{	double sum = 0.;
				size_t middle, im_left, mj_right, ij_result;
				for(middle = 0; middle < n_middle_; middle++)
				{	im_left  = left(i, middle, order_left);
					mj_right = right(middle, j, order_right);
					sum     += tx[im_left] * tx[mj_right];
				}
				ij_result = result(i, j, order_result);
				ty[ ij_result ] += sum;
			}
		}
		return;
	}
/* %$$

$head Reverse Partials One Order$$
Compute reverse mode partials for one order and sum into px:
$srccode%cpp% */
	void reverse_multiply(
		size_t                order_left ,
		size_t                order_right,
		const vector<double>&         tx ,
		const vector<double>&         ty ,
		vector<double>&               px ,
		const vector<double>&         py )
	{	size_t i, j;
		size_t order_result = order_left + order_right;
		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
			{	size_t middle, im_left, mj_right, ij_result;
				for(middle = 0; middle < n_middle_; middle++)
				{	ij_result = result(i, j, order_result);
					im_left   = left(i, middle, order_left);
					mj_right  = right(middle, j, order_right);
					// sum       += tx[im_left]  * tx[mj_right];
					px[im_left]  += tx[mj_right] * py[ij_result];
					px[mj_right] += tx[im_left]  * py[ij_result];
				}
			}
		}
		return;
	}
/* %$$
$head Set Union$$
$srccode%cpp% */
	using CppAD::set_union;
/* %$$

$head CppAD User Atomic Callback Functions$$
$srccode%cpp% */
	// ----------------------------------------------------------------------
	// forward mode routine called by CppAD
	bool mat_mul_forward(
		size_t                   id ,
		size_t                    k ,
		size_t                    n ,
		size_t                    m ,
		const vector<bool>&      vx ,
		vector<bool>&            vy ,
		const vector<double>&    tx ,
		vector<double>&          ty
	)
	{	size_t i, j, ell;
		get_info(id, k, n, m);

		// check if this is during the call to mat_mul(id, ax, ay)
		if( vx.size() > 0 )
		{	assert( k == 0 && vx.size() > 0 );

			// store the vx information in info_
			assert( vx_->size() == 0 );
			info_[id].vx.resize(n);
			for(j = 0; j < n; j++)
				info_[id].vx[j] = vx[j];
			assert( vx_->size() == n );

			// now compute vy
			for(i = 0; i < nr_result_; i++)
			{	for(j = 0; j < nc_result_; j++)
				{	// compute vy[ result(i, j, 0) ]
					bool   var = false;
					bool   nz_left, nz_right;
					size_t middle, im_left, mj_right, ij_result;
					for(middle = 0; middle < n_middle_; middle++)
					{	im_left  = left(i, middle, k);
						mj_right = right(middle, j, k);
						nz_left  = vx[im_left]  | (tx[im_left] != 0.);
						nz_right = vx[mj_right] | (tx[mj_right]!= 0.);
						// if not multiplying by the constant zero
						if( nz_left & nz_right )
							var |= (vx[im_left] | vx[mj_right]);
					}
					ij_result     = result(i, j, k);
					vy[ij_result] = var;
				}
			}
		}

		// initialize result as zero
		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
				ty[ result(i, j, k) ] = 0.;
		}
		// sum the product of proper orders
		for(ell = 0; ell <=k; ell++)
			multiply_and_sum(ell, k-ell, tx, ty);

		// All orders are implemented and there are no possible error
		// conditions, so always return true.
		return true;
	}
	// ----------------------------------------------------------------------
	// reverse mode routine called by CppAD
	bool mat_mul_reverse(
		size_t                   id ,
		size_t                    k ,
		size_t                    n ,
		size_t                    m ,
		const vector<double>&    tx ,
		const vector<double>&    ty ,
		vector<double>&          px ,
		const vector<double>&    py
	)
	{	get_info(id, k, n, m);

		size_t ell = n * n_order_;
		while(ell--)
			px[ell] = 0.;

		size_t order = n_order_;
		while(order--)
		{	// reverse sum the products for specified order
			for(ell = 0; ell <=order; ell++)
				reverse_multiply(ell, order-ell, tx, ty, px, py);
		}

		// All orders are implemented and there are no possible error
		// conditions, so always return true.
		return true;
	}

	// ----------------------------------------------------------------------
	// forward Jacobian sparsity routine called by CppAD
	bool mat_mul_for_jac_sparse(
		size_t                               id ,
		size_t                                n ,
		size_t                                m ,
		size_t                                p ,
		const vector< std::set<size_t> >&     r ,
		vector< std::set<size_t> >&           s )
	{	size_t i, j, k, im_left, middle, mj_right, ij_result;
		k = 0;
		get_info(id, k, n, m);

		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
			{	ij_result = result(i, j, k);
				s[ij_result].clear();
				for(middle = 0; middle < n_middle_; middle++)
				{	im_left   = left(i, middle, k);
					mj_right  = right(middle, j, k);

					// s[ij_result] = union( s[ij_result], r[im_left] )
					s[ij_result] = set_union(s[ij_result], r[im_left]);

					// s[ij_result] = union( s[ij_result], r[mj_right] )
					s[ij_result] = set_union(s[ij_result], r[mj_right]);
				}
			}
		}
		return true;
	}
	// ----------------------------------------------------------------------
	// reverse Jacobian sparsity routine called by CppAD
	bool mat_mul_rev_jac_sparse(
		size_t                               id ,
		size_t                                n ,
		size_t                                m ,
		size_t                                p ,
		vector< std::set<size_t> >&           r ,
		const vector< std::set<size_t> >&     s )
	{	size_t i, j, k, im_left, middle, mj_right, ij_result;
		k = 0;
		get_info(id, k, n, m);

		for(j = 0; j < n; j++)
			r[j].clear();

		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
			{	ij_result = result(i, j, k);
				for(middle = 0; middle < n_middle_; middle++)
				{	im_left   = left(i, middle, k);
					mj_right  = right(middle, j, k);

					// r[im_left] = union( r[im_left], s[ij_result] )
					r[im_left] = set_union(r[im_left], s[ij_result]);

					// r[mj_right] = union( r[mj_right], s[ij_result] )
					r[mj_right] = set_union(r[mj_right], s[ij_result]);
				}
			}
		}
		return true;
	}
	// ----------------------------------------------------------------------
	// reverse Hessian sparsity routine called by CppAD
	bool mat_mul_rev_hes_sparse(
		size_t                               id ,
		size_t                                n ,
		size_t                                m ,
		size_t                                p ,
		const vector< std::set<size_t> >&     r ,
		const vector<bool>&                   s ,
		vector<bool>&                         t ,
		const vector< std::set<size_t> >&     u ,
		vector< std::set<size_t> >&           v )
	{	size_t i, j, k, im_left, middle, mj_right, ij_result;
		k = 0;
		get_info(id, k, n, m);

		for(j = 0; j < n; j++)
		{	t[j] = false;
			v[j].clear();
		}

		assert( vx_->size() == n );
		for(i = 0; i < nr_result_; i++)
		{	for(j = 0; j < nc_result_; j++)
			{	ij_result = result(i, j, k);
				for(middle = 0; middle < n_middle_; middle++)
				{	im_left   = left(i, middle, k);
					mj_right  = right(middle, j, k);

					// back propagate Jacobian sparsity
					t[im_left]   = (t[im_left] | s[ij_result]);
					t[mj_right]  = (t[mj_right] | s[ij_result]);
					// Visual Studio C++ 2008 warns unsafe mix of int and
					// bool if we use the following code directly above:
					// t[im_left]  |= s[ij_result];
					// t[mj_right] |= s[ij_result];

					// back propagate Hessian sparsity
					// v[im_left]  = union( v[im_left],  u[ij_result] )
					// v[mj_right] = union( v[mj_right], u[ij_result] )
					v[im_left] = set_union(v[im_left],  u[ij_result] );
					v[mj_right] = set_union(v[mj_right], u[ij_result] );

					// Check for case where the (i,j) result element
					// is in reverse Jacobian and both left and right
					// operands in multiplication are variables
					if(s[ij_result] & (*vx_)[im_left] & (*vx_)[mj_right])
					{	// v[im_left] = union( v[im_left], r[mj_right] )
						v[im_left] = set_union(v[im_left], r[mj_right] );
						// v[mj_right] = union( v[mj_right], r[im_left] )
						v[mj_right] = set_union(v[mj_right], r[im_left] );
					}
				}
			}
		}
		return true;
	}
/* %$$

$head Declare mat_mul Function$$
Declare the $code AD<double>$$ routine $codei%mat_mul(%id%, %ax%, %ay%)%$$
and end empty namespace
(we could use any $cref/simple vector template class/SimpleVector/$$
instead of $code CppAD::vector$$):
$srccode%cpp% */
	CPPAD_USER_ATOMIC(
		mat_mul                 ,
		CppAD::vector           ,
		double                  ,
		mat_mul_forward         ,
		mat_mul_reverse         ,
		mat_mul_for_jac_sparse  ,
		mat_mul_rev_jac_sparse  ,
		mat_mul_rev_hes_sparse
	)
} // End empty namespace
/* %$$
$end
*/

# endif
